6 citations · 8 across the 6 of their papers we have counts for
3 papers · 1 filter
Automatic Machine Learning Derived from Scholarly Big Data
Asnat Greenstein-Messica, Roman Vainshtein, Gilad Katz +2
One of the challenging aspects of applying machine learning is the need to identify the algorithms that will perform best for a given dataset. This process can be difficult, time c…
RankML: a Meta Learning-Based Approach for Pre-Ranking Machine Learning Pipelines
Doron Laadan, Roman Vainshtein, Yarden Curiel +2
The explosion of digital data has created multiple opportunities for organizations and individuals to leverage machine learning (ML) to transform the way they operate. However, the…
DeepLine: AutoML Tool for Pipelines Generation using Deep Reinforcement Learning and Hierarchical Actions Filtering
Yuval Heffetz, Roman Vainstein, Gilad Katz +1
Automatic machine learning (AutoML) is an area of research aimed at automating machine learning (ML) activities that currently require human experts. One of the most challenging ta…